Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Modeling Users' Dynamic Preference for Personalized Recommendation
Authors: Xin Liu
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 15,718 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,196 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted over two real datasets demonstrate that our approach outperforms the state-of-the-art recommendation models by at least 42.46% and 66.14% in terms of precision and Mean Reciprocal Rank respectively. |
| Researcher Affiliation | Academia | Xin Liu Institute for Infocomm Research (I2R) Singapore EMAIL |
| Pseudocode | No | The paper describes the model in text and mathematical equations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is publicly available. |
| Open Datasets | Yes | The ο¬rst dataset we used was collected from Delicious (http://www.delicious.com). The data [Cantador et al., 2011] contains 1,867 users and 69,226 URLs... We also used the data collected from Last.fm (http://www. lastfm.com). The data [Cantador et al., 2011] records the information of 1,892 users listening history... |
| Dataset Splits | Yes | The parameters of MF based models such as latent factor vector dimensionality, learning rate, regularization terms are determined by 5-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The hyperparameters h, Ξ» and Ο are initialized to 1. ... The parameters of MF based models such as latent factor vector dimensionality, learning rate, regularization terms are determined by 5-fold cross validation. ... for both datasets, 10 topics achieve the highest precision and MRR. In the following experiments, we use 10 topics for all top modeling based models. |